The current focus is on vision-aided inertial navigation methods that provide estimates by fusing measurements from a camera and an inertial measurement unit (IMU). In recent years, several algorithms of this kind have been proposed, tailored for different applications. For instance, if features with known coordinates are available, map-based localization algorithms can be used to provide absolute-pose estimates However, environments with a priori known feature maps are not abundant, and therefore it is crucial to develop methods that enable operation in unknown environments, using naturally-occurring features whose locations in the world are not known in advance.
A variety of methods have been proposed for this task, ranging from simultaneous localization and mapping, to pairwise image-based displacement estimation, as well as multi-frame methods that employ the feature measurements to impose constraints on a number of camera poses. The two most significant characteristics of any method are the accuracy it can achieve, and the computational resources it requires. Typically, simple and approximate methods have low cost but yield low accuracy, while more computationally complex methods can attain higher estimation precision. Developing methods that attain high precision at low computational cost, and that are robust enough to operate in real-world settings, remains challenging.
Therefore, there is a need for a method for tracking the motion state (e.g., position and orientation) of a platform using inertial measurements and observations of features with unknown locations, overcoming the limitations of the prior art.